A PRELIMINARY DISCUSSION ON PUBLIC PARTICIPATION IN LAND USE PLANING:TAKING JIASHAN COUNTY AS AN EXAMPLE
Bibliographic record
Abstract
Public Participation is an important process in making a regional sustainable land use planning, but it is still in its beginning stages in China. Based on the international cooperation on the revision of comprehensive land use planning in Jiashan County, Zhejiang Province, between China Ministry of Land Resources and Canadian Institute of Planners, this paper has discussed the attributes which contribute to the quality of life, as well as challenges to improving quality of life in the brainstorming workshop. Individually every participant is asked to rank the importance of the attributes and challenges that are identified as affecting the quality of life in their discussion group. Every member has $100 to spend each to resolve the challenges and take advantage of opportunities needed the greatest attention in the future planning. Those are of highest importance should be allocated more money. Finally, top five attributes challenges are identified as guiding principles for the land use planning, which are good location, abundant historical culture and heritage, polluted water and air, scattered urbanization and decentralized, agricultural land preservation policies of the central government lack flexibility. After that, the specific actions are identified which will be required to establish a sustainable land use planning for Jiashan County.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".